Technical Field
The present disclosure relates to systems, devices and methods for treating neurological disorders, e.g., epilepsy, with closed-loop (responsive) neurostimulation. More particularly, the disclosed embodiments relate to systems, devices and methods for defining the stimulation to be delivered based at least in part on one or more of a type of neurological event detected, a state of a patient measured from physiological data elements; and comparison to the clinical effectiveness of a prior course of stimulation therapy, such that the therapeutic benefit of the stimulation therapy can be optimized for the patient.
Background
Neurostimulation therapy, increasingly that which is delivered by an implanted pulse generator or neurostimulator, is being used for various chronic diseases and neurological disorders, such as epilepsy, pain management, and movement disorders such as Parkinson's disease. An objective of the stimulation is to modulate the patient's neural tissue in some desired way to either treat the disease or disorder itself or to change a symptom of the disease or disorder. For example, for epilepsy, an objective may be to reduce the number of seizures the patient experiences, and for a movement disorder, an objective may be to reduce tremor. Depending on the objective, the stimulation may be intended to increase neural activity or inhibit it. Metrics relative to the objective of the therapy can be used to measure the effectiveness of the therapy.
In an implantable neurostimulator, stimulation therapy typically involves generating a train of pulses of electrical stimulation (current-controlled or voltage-controlled) and applying them in a “burst” to neural tissue of the patient through electrodes. The pulses are usually “charge balanced” for safety, to minimize an undesirable build-up of charge at the electrode-to-tissue interface. The therapy is characterized by parameters, the values of which define what each instance of therapy will comprise. For example, the parameters may include the magnitude of the pulse, or pulse amplitude, the width of the pulse, and the time between bursts of pulses. The neurostimulator will allow each of these parameters to have a value in a range of values. Collectively, parameters and the range of possible values for each may be referred to as the “parameter space” for the stimulation the neurostimulator is configurable to deliver. When a value is specified for each parameter in the parameter space, this is referred to herein as a “stimulation parameter set.”
Implantable neurostimulators are known that not only generate stimulation according to stimulation parameter sets and then deliver it through electrodes, but also sense and monitor electrographic signals from the patient. These neurostimulators can be configured to look for certain patterns in the sensed signals and, if one of those patterns is detected, use the fact of detection as a trigger to generate and deliver the stimulation. NeuroPace, Inc. (Mountain View, Calif.) manufactures and sells one such neurostimulator under the trade name “RNS” for treating epilepsy. The neurostimulator subjects the sensed signals to one or more tools or processes which extract features and analyze the extracted features, alone or in combination, to determine when one of the patterns should be deemed to have been detected as a neurological event. Each pattern can be classified as a different neurological event type. The neurostimulator can be configured to deliver stimulation according to a particular stimulation parameter set whenever a neurological event is detected.
In epilepsy, it is generally desirable to treat a seizure at or near the time it begins electrographically, or at or during its onset. Accordingly, it is desirable to configure a neurostimulator to detect neurological events that comprise a type of seizure onset for the patient. When stimulation therapy is applied to neural tissue that is evidencing a type of electrographic seizure onset, the stimulation may interrupt the developing seizure and either arrest it altogether or reduce its severity, improving the overall condition of the patient. Thus, the overall condition of the patient also can be used as a measure of the clinical effectiveness of a stimulation therapy delivered by an implantable neurostimulator. Similarly, a metric that can be used as a measure of the clinical effectiveness of the therapy may be the percent reduction in the number of frequency of seizures experienced by the patient. These seizures may counted based on reports of seizures by the patient or the patient's caregiver (e.g., a seizure ‘diary’ for the patient), or they may be counted using some proxy for a seizure, such as the length of time electrographic activity measured from a patient and corresponding to a seizure persists.
When the parameter space for the stimulation is relatively large, then choosing values for each stimulation parameter to create a parameter set can be challenging. Even when there is a reliable way of detecting the neurological event type to be used to trigger delivery of stimulation, determining which stimulation parameter is optimal for that event type has been an empirical process, involving a lot of trial and error.
Finding a parameter set that optimizes the clinical effectiveness of a stimulation therapy for a patient can be challenging. Sometimes a patient has to visit the physician many times to try new parameter sets before identifying one that is effective (e.g. reduces the frequency of reported seizures by ≥50%). Unless the patient happens to experience one of the neurological events the neurostimulator is configured to detect when the patient is in the doctor's office, then the process involves adjusting the parameter set, sending the patient home to see how he does for a while, then bringing him back in. If the patient has not been doing well, further adjustments will be made—that is, a new stimulation parameter set will be tried—and so on and so forth until a parameter set is arrived at that seems to work well (be clinically effective). The time between visits is usually at least a few weeks or longer. Thus this empirical process is not ideal and, for some patients, it may take a long period of trying many different stimulation parameter sets before arriving at one that works well. Moreover, since it is practically impossible to try out every available stimulation parameter subspace and stimulation parameter sets within a subspace, an optimal subspace or set for a patient may never be discovered. Thus, being able to automatically identify an optimal stimulation subspace or stimulation parameter set for a patient would be a significant improvement over the currently available responsive neurostimulation systems.